Multicoin Capital: What entrepreneurial opportunities are there in prediction markets?

In the age of information explosion, attention is becoming a measurable and tradable asset. This article will explore how attention assets can be financialized through prediction markets. The article is based on a piece written by Multicoin Capital and organized, translated, and authored by BlockBeats. (Background: $100 investment: prediction market vs. memecoin, which has greater opportunity?) (Background Supplement: a16 long article: prediction markets are becoming a new meme and traffic password) Editor's Note: In the age of information explosion, attention itself is becoming a measurable and tradable asset. This article explores how “attention assets” can be financialized through prediction markets and Oracle Machine mechanisms, and proposes a methodology for constructing “Attention Perpetual Futures.” As the value of memes gradually emerges in traditional assets like stocks, we may be on the threshold of attention economics becoming a mainstream asset class. The following is the original content: Financial Potential of Attention Assets Although it may be overly simplistic, assets can be broadly categorized into two types: 1. Cash flow assets - mainly including stocks and bonds. These assets generate cash flow, based on which investors make valuations. 2. Supply and demand assets - mainly applicable to commodities and forex. Their prices fluctuate with supply and demand. Recently, the crypto space has birthed a new type of asset - attention-based assets. Today, “attention assets” mainly refer to user-generated assets (UGAs), such as NFTs, creator tokens, and memecoins. These assets serve as a “Shearing Point” of cultural attention, with their prices reflecting the rise and fall of attention. From a cultural perspective, memecoins are interesting, but from a financial perspective, they still have many shortcomings. Efficient attention assets should allow market participants to gain direct financial exposure to the attention of something. This way, participants will be incentivized to trade assets they believe are mispriced, and the market can form prices reflecting attention forecasts through collective behavior. We believe that, if constructed properly, attention assets have the potential to become a true asset class. To promote this concept, this article introduces the idea of “Attention Oracles,” a new type of oracle structure that supports “Attention Perpetual Futures” - a new financial instrument that allows traders to go long or short on cultural attention. In short, attention oracles build a weighted aggregate index that captures changes in attention by using binary prediction markets around a particular topic, utilizing dimensions such as price, liquidity, forecast time, and importance. To ensure its effective operation, the underlying markets must be carefully selected to represent relevant attention inputs in the real world. Using prediction markets as inputs for attention oracles also introduces “embedded manipulation costs,” which theoretically can reduce malicious manipulation, as antagonistic traders must bear capital risk to influence the index. Why do we need Attention Perpetual Futures? UGAs have found product-market fit in pure speculation and are very good at tracking zero-to-one attention, such as emerging internet trends and memes. The problem UGAs solve is creating assets for things that the traditional financial system cannot cover. The traditional asset issuance process is slow, expensive, and has high regulatory thresholds, limiting the scope of assets that can be issued. Attention assets must operate at internet speed to keep up with global cultural trends. Combined with permissionless token issuance mechanisms, clever pricing methods like bonding curves, and liquidity support from DEXs, almost anyone can create assets for free, initiate liquidity, and push them to the market for others to trade. An observation about UGAs is that their prices usually start from zero. This is not a flaw, but a feature. Because if you create a new meme from zero, then its attention at birth is zero, and entering at a low price makes sense. This also allows those skilled at early trend spotting to monetize their abilities by creating low-cost assets. But this also means that UGAs are not suitable for providing financial exposure for things that already have a lot of attention. For example, suppose you want to go long on LeBron James's attention. You can create a memecoin, but there are already many LeBron tokens on the market; which one should you buy? Additionally, a new LeBron memecoin's price would be close to zero, but LeBron is one of the most well-known figures globally, and his attention should be very high; it should not surge 100 times in a short time. Finally, if you want to short his attention, memecoins also find it difficult to achieve this. So, what should assets targeting high-attention topics look like? Some requirements may include: 1. Bidirectional trading capability: assets should support going long and short. 2. Connection to real-world attention data sources: there must be reliable “fact sources” to measure attention. 3. Should not start from zero: assets should have initial value reflecting existing attention. If you examine these needs, you will quickly find that perpetual futures (Perps) are very fitting: they are bidirectional, have oracles (fact sources), and as derivatives, do not start from zero. The real challenge is how to build an effective oracle for “Attention Perpetual Futures.” Currently, some teams are researching this issue, such as Noise. On the Noise platform, traders can go long or short on the “mindshare” of certain crypto projects (like MegaETH and Monad). Noise uses Kaito as the oracle, which aggregates social media and news data to generate a number representing the level of attention for a particular topic. However, this design still has room for improvement. The goal of the attention oracle is to use attention-related data as input, apply certain functions to process this data, and output a value for traders to go long or short. One problem with using social media as input is that social media can easily be manipulated. This reflects a variant of Goodhart's Law: in adversarial markets, traders will attempt to manipulate input data to influence prices. Kaito has had to redesign their rankings and anti-spam mechanisms to address this issue. Furthermore, social media does not perfectly capture attention. Taking Shohei Ohtani as an example, he has a global fan base and uses various different social media applications, but these platforms may not all be indexed by Kaito. If he wins the World Series again, his star aura will be stronger, but his fan count or social media mentions may not necessarily grow linearly. Attention Oracles: A Market-Based Approach Returning to our earlier example of LeBron James, suppose you want to trade LeBron's attention. To build an attention oracle for him, the first step is to gather (or create, if it does not exist yet) multiple binary prediction markets about LeBron, such as: “Will LeBron James have over X million followers by the end of this month?”; “Will LeBron James win a championship in 2026?”; “Will LeBron James win the MVP in 2026?” A well-developed LeBron attention oracle would use more underlying markets, but for the sake of example, we will temporarily use these three. The index price will be calculated by performing a weighted aggregation of the prices, liquidity, resolution time, and importance of each market. For each market, we have prices, liquidity, resolution times, and an importance rating. For simplicity, we use a very simple weight calculation formula…